Literature DB >> 32480967

Development and evaluation of a field-based high-throughput phenotyping platform.

Pedro Andrade-Sanchez1, Michael A Gore2, John T Heun1, Kelly R Thorp2, A Elizabete Carmo-Silva2, Andrew N French2, Michael E Salvucci2, Jeffrey W White2.   

Abstract

Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84ha h-1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H2=0.86-0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2=0.28-0.90) and temperature (H2=0.01-0.90) traits. We also found a strong agreement (r2=0.35-0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.

Entities:  

Year:  2013        PMID: 32480967     DOI: 10.1071/FP13126

Source DB:  PubMed          Journal:  Funct Plant Biol        ISSN: 1445-4416            Impact factor:   3.101


  16 in total

Review 1.  Capturing crop adaptation to abiotic stress using image-based technologies.

Authors:  Nadia Al-Tamimi; Patrick Langan; Villő Bernád; Jason Walsh; Eleni Mangina; Sónia Negrão
Journal:  Open Biol       Date:  2022-06-22       Impact factor: 7.124

2.  Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding.

Authors:  Ivan Chapu; David Kalule Okello; Robert C Ongom Okello; Thomas Lapaka Odong; Sayantan Sarkar; Maria Balota
Journal:  Front Plant Sci       Date:  2022-06-14       Impact factor: 6.627

3.  Robust mosaicking of maize fields from aerial imagery.

Authors:  Rumana Aktar; Dewi Endah Kharismawati; Kannappan Palaniappan; Hadi Aliakbarpour; Filiz Bunyak; Ann E Stapleton; Toni Kazic
Journal:  Appl Plant Sci       Date:  2020-09-10       Impact factor: 1.936

Review 4.  Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.

Authors:  Giao N Nguyen; Sally L Norton
Journal:  Plants (Basel)       Date:  2020-06-29

5.  High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network.

Authors:  Yinglun Li; Weiliang Wen; Xinyu Guo; Zetao Yu; Shenghao Gu; Haipeng Yan; Chunjiang Zhao
Journal:  PLoS One       Date:  2021-01-12       Impact factor: 3.240

Review 6.  Review: Application of Artificial Intelligence in Phenomics.

Authors:  Shona Nabwire; Hyun-Kwon Suh; Moon S Kim; Insuck Baek; Byoung-Kwan Cho
Journal:  Sensors (Basel)       Date:  2021-06-25       Impact factor: 3.576

Review 7.  Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives.

Authors:  Abbas Atefi; Yufeng Ge; Santosh Pitla; James Schnable
Journal:  Front Plant Sci       Date:  2021-06-25       Impact factor: 5.753

Review 8.  Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops.

Authors:  Marouane Baslam; Toshiaki Mitsui; Michael Hodges; Eckart Priesack; Matthew T Herritt; Iker Aranjuelo; Álvaro Sanz-Sáez
Journal:  Front Plant Sci       Date:  2020-07-06       Impact factor: 5.753

9.  Gloxinia-An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants.

Authors:  Olivier Pieters; Tom De Swaef; Peter Lootens; Michiel Stock; Isabel Roldán-Ruiz; Francis Wyffels
Journal:  Sensors (Basel)       Date:  2020-05-28       Impact factor: 3.576

10.  Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset.

Authors:  Radek Zenkl; Radu Timofte; Norbert Kirchgessner; Lukas Roth; Andreas Hund; Luc Van Gool; Achim Walter; Helge Aasen
Journal:  Front Plant Sci       Date:  2022-01-04       Impact factor: 5.753

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